Complex method but …….

Dynamic regression can be used when (1) the data is long enough and stable enough to support a correlation model (2) the introduction of explanatory variables results in a significant improvement in model accuracy and (3) valid predictions are available for the explanatory variables. If some companies, which have the means to use dynamic regression models, are sometimes reluctant to use this modeling approach, it is because they know from experience that complex models often produce forecasts that are less good. than simpler models, even though they fit the observed data better.

Unlike univariate time series forecasting models (exponential smoothing, Box & Jenkins, ….), dynamic regression is a forecasting technique that allows highlighting the dynamic and causal effects between explanatory and lagged variables in the weather.

However, like the Box-Jenkins model, the dynamic regression model has a certain level of sophistication and has so far been difficult to identify and very time-consuming to construct. This has reduced its use in business forecasting.

….. but now easely implementable thanks to automation algorithms

Automation algorithms are now included in several forecasting software, in particular in the forecasting software “Forecast Pro”. They now allow forecasters to quickly and more easily build adaptable and more easily operational dynamic regression models. This has resulted in a greater use of this type of models.

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